In this paper, we introduce an alternative approach to enhancing Multi-Agent Reinforcement Learning (MARL) through the integration of domain knowledge and attention-based policy mechanisms. Our methodology focuses on the incorporation of domain-specific expertise into the learning process, which simplifies the development of collaborative behaviors. This approach aims to reduce the complexity and learning overhead typically associated with MARL by enabling agents to concentrate on essential aspects of complex tasks, thus optimizing the learning curve. The utilization of attention mechanisms plays a key role in our model. It allows for the effective processing of dynamic context data and nuanced agent interactions, leading to more refined decision-making. Applied in standard MARL scenarios, such as the Stanford Intelligent Systems Laboratory (SISL) Pursuit and Multi-Particle Environments (MPE) Simple Spread, our method has been shown to improve both learning efficiency and the effectiveness of collaborative behaviors. The results indicate that our attention-based approach can be a viable approach for improving the efficiency of MARL training process, integrating domain-specific knowledge at the action level.

翻译：本文提出了一种通过整合领域知识与基于注意力的策略机制来增强多智能体强化学习（MARL）的替代方法。我们的方法侧重于将领域特定专业知识融入学习过程，从而简化协作行为的开发。该方法旨在通过使智能体专注于复杂任务的关键方面，降低MARL通常伴随的复杂性和学习开销，从而优化学习曲线。注意力机制的利用在我们模型中扮演着关键角色，它能有效处理动态上下文数据和微妙的智能体交互，进而实现更精细的决策。在标准MARL场景（如斯坦福智能系统实验室（SISL）追捕问题和多粒子环境（MPE）Simple Spread）中的应用表明，我们的方法同时提高了学习效率和协作行为的有效性。结果表明，基于注意力的方法可通过在行动层面整合领域特定知识，成为提升MARL训练过程效率的可行途径。